Director of AI Engineering

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Job Summary
The Director of AI Engineering will lead the design, development, and deployment of artificial intelligence and machine learning systems while providing technical and organizational leadership for AI engineering initiatives. The role will build and grow a high-performing team of AI/ML engineers, MLOps engineers, and applied scientists; establish technical direction and engineering standards; and partner with Product, Data Science, and executive leadership to translate AI capabilities into business outcomes. The role will oversee the full AI system lifecycle, from research and prototyping through production deployment, monitoring, and continuous improvement.


Key Responsibilities
• Lead, mentor, and grow a team of AI/ML engineers, MLOps engineers, and applied scientists, including hiring, performance management, and career development.
• Define and drive the technical roadmap for AI/ML systems while aligning engineering priorities with company strategy and product goals.
• Oversee the full lifecycle of AI systems, including research, prototyping, model training, evaluation, productionization, deployment, monitoring, and iteration.
• Architect scalable and reliable infrastructure for training and serving machine learning models, including LLMs and generative AI solutions where applicable.
• Establish engineering best practices for MLOps, including model CI/CD, experiment tracking, versioning, testing, and observability.
• Partner with Product Management to identify high-value AI use cases and translate business requirements into technical solutions.
• Collaborate with Data Engineering and Data Science teams to ensure high-quality data pipelines and feature availability.
• Own AI system performance, cost, latency, and reliability targets and drive continuous improvement.
• Evaluate and integrate third-party AI/ML tools, platforms, and APIs, including cloud ML platforms and foundation model providers.
• Establish and maintain responsible AI practices covering model governance, bias mitigation, security, and applicable regulatory requirements.
• Communicate technical strategy, progress, risks, and priorities to executive leadership and non-technical stakeholders.
• Stay current with emerging AI/ML research, technologies, tools, and industry trends and evaluate their applicability to business needs.
• Manage AI engineering budgets, headcount planning, and cross-functional technical roadmaps.
• Provide technical leadership and guidance across AI engineering initiatives and promote scalable engineering practices.


Required Qualifications
• 8+ years of experience in software engineering or ML engineering, including 3+ years in technical leadership or engineering management.
• Proven experience leading teams that have delivered machine learning or AI systems into production at scale.
• Strong hands-on background in machine learning, deep learning, or applied AI, preferably with prior experience as an ML/AI engineer.
• Experience with modern ML frameworks such as PyTorch and TensorFlow.
• Experience with MLOps tools and platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI.
• Strong understanding of software engineering fundamentals, including distributed systems, APIs, and cloud infrastructure.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization technologies such as Docker and Kubernetes.
• Experience managing budgets, headcount planning, and cross-functional engineering roadmaps.
• Excellent communication skills with the ability to explain complex technical concepts to technical and non-technical stakeholders.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.


Preferred Qualifications
• Master's or PhD in Computer Science, Machine Learning, or a related field.
• Experience building or deploying products using large language models (LLMs) or generative AI.
• Experience with retrieval-augmented generation (RAG), model fine-tuning, or prompt engineering at scale.
• Experience working in regulated industries such as financial services or healthcare.
• Knowledge of AI governance and compliance frameworks.
• Experience scaling an AI/ML engineering organization from a small team to a larger organization.